Image segmentation is one of the most fundamental and challenging problem in image processing. Considering the user involvement during the segmentation process, there are three main categories, i.e., fully automatic, semi-automatic and manual methods. In general, the three categories exhibit increasing levels of segmentation accuracy and flexibility at the expense of user interactions. The interactive segmentation framework proposed in this work falls into the second category. In general, the computer implemented method of the present invention achieves segmentation after the user provides a set of markings which roughly label the regions to be extracted. This type of segmentation methods are found very desirable for complex images as well as subjective applications.
A number of interactive segmentation algorithms have been introduced in the literature, which include Graph Cut based methods [1] [2], Geodesic method [3] [4], and Random Walks based methods[5][6]. All these algorithms treat the image as a weighted graph with nodes corresponding to the pixels in the images and edge being placed between neighboring pixels. A certain energy function is defined and minimized on this graph to generate the segmentation. In recent years, superpixel [10] based algorithms were used increasingly [7] [8] [9]. By using superpixel technique, pixels are grouped into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. Superpixels capture image redundancy, provide a convenient primitive from which to compute image features, and greatly reduce the complexity of subsequent image processing tasks [10].
Typically, whole slide digital images have enormous data densities, and characterized by their heterogeneity and histopathology contents. For example, a typical IHC stained image for breast tumor tissue digitized at 20× resolution can have 30,000×30,000 elements, which is approximately 900million pixels in total for a single image. With respect to superpixels, larger superpixel segments provide richer features but may result in under segmentation. Conversely, smaller scale segments have less discriminative features but usually offer better boundary fit [11].
Image segmentation is a computationally demanding task. Nevertheless, it is desirable to have a segmentation method where users can provide input according to the image at hand.